Marketing Simulations: Review and Prospects

Quantitative tools came into marketing largely as a potential means for helping marketing executives make decisions in areas of high uncertainty, such as launching products, setting advertising budgets, and designing sales-force strategies. Early model building in marketing was characterized by an effort to impose standard operations including research tools, calculus, linear programming, queuing theory and Markov processes on highly simplified versions of marketing problems. This article will undertake to review and evaluate a selected sample of marketing simulations. The term simulation is interpreted broadly to reflect the fact that it is used in a variety of ways by persons inside and outside of marketing to describe their work. In the most general sense, simulation describes the act of creating a complex model to resemble a real process or system, and running and experimenting with the model in the hope of learning something about the real system. Simulation is used as behavioral modeling, as a way of introducing and handling uncertainty, as a computational technique for measuring parametric sensitivity and as a heuristic technique for finding an approximately optimal solution.